class_docstr: The generalised normal has a shape parameter beta that controls the tails of
  the pdf. When beta=2 we recover the Gaussian, beta = 1 is Laplace, beta->inf
  is uniform.
  
  https://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.stats.gennorm.html
code: def __call__(self, shape):
      if self.apply_scale:
          sqrt_n = np.sqrt(shape[1])
      else:
          sqrt_n = 1
  
      D = gennorm.rvs(beta=self.beta, size=shape)
      # This is the variance of d
      var = gamma(3.0 / self.beta) / gamma(1.0 / self.beta)
      # Make D have unit variance
      D = D / np.sqrt(var)
  
      # Return correctly scaled version of D
      return D / sqrt_n * self.std
  
imports: import numpy
  import abc
  from scipy.stats import gennorm
  from scipy.special import gamma
  from scipy.stats import laplace
label_desc: Use image node_id 2 for calling the GeneralisedNormal obj's underlying member method code with example usage: obj.__call__(shape) and returns: unknown